Exam prefetching based on subject anatomy

ABSTRACT

Inference of appropriate anatomical region from inconsistent descriptions in order to provide fast and accurate prefetching is provided. In various embodiments, a first plurality of user-configurable rules is read from a data store. Each rule maps to a user-configurable anatomical region. A plurality of studies is accessed from the image archive. Each of the plurality of studies has associated metadata. The plurality of rules is applied to the metadata associated with the plurality of studies to determine an anatomical region of each of the plurality of studies. Based on the anatomical regions of the plurality of studies and one or more additional rule, a subset of the plurality of studies is selected for display to a user on a display.

BACKGROUND

Embodiments of the present invention relate to prefetching examinations, and more specifically, to inferring appropriate anatomical region from inconsistent descriptions in order to provide fast and accurate prefetching.

BRIEF SUMMARY

According to embodiments of the present disclosure, methods of and computer program products for prefetching examinations are provided. A plurality of keywords is read. Each of the plurality of keywords corresponds to an anatomical region. A plurality of studies is accessed. Each of the plurality of studies has at least one associated body part and an examination description. Based upon the associated body part and examination description of each of the plurality of studies, the plurality of keywords is updated. In response to a request for a first medical imaging study, a plurality of studies having a patient in common with the first medical imaging study is accessed using the plurality of keywords. Those of the plurality of studies related to the first medical imaging study by one or more rule are selected. The selected studies are pre-fetched for display to a user.

In some embodiments, methods of and computer program products for medical image access are provided. A first plurality of user-configurable rules is read from a data store. Each rule maps to a user-configurable anatomical region. A plurality of studies is accessed from the image archive. Each of the plurality of studies has associated metadata. The plurality of rules is applied to the metadata associated with the plurality of studies to determine an anatomical region of each of the plurality of studies. Based on the anatomical regions of the plurality of studies and one or more additional rule, a subset of the plurality of studies is selected for display to a user on a display.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 depicts an exemplary Picture Archiving and Communication System.

FIG. 2 illustrates an exemplary clinical image search and retrieval method.

FIG. 3 is a schematic view of anatomical matching according to embodiments of the present disclosure.

FIG. 4 depicts an exemplary user interface for the configuration of anatomical regions and associated keywords according to embodiments of the present disclosure.

FIG. 5 depicts an exemplary report output according to embodiments of the present disclosure.

FIG. 6 depicts an exemplary user interface for configuring pre-fetch rules according to embodiments of the present disclosure.

FIG. 7 depicts an exemplary user interface for configuring pre-fetch rules according to embodiments of the present disclosure.

FIG. 8 illustrates a method for examination prefetching according to embodiments of the present disclosure.

FIG. 9 illustrates a method for examination prefetching according to embodiments of the present disclosure.

FIG. 10 illustrates a method for medical image access according to embodiments of the present disclosure.

FIG. 11 depicts a computing node according to an embodiment of the present invention.

DETAILED DESCRIPTION

A Picture Archiving and Communication System (PACS) is a medical imaging system that provides storage and access to images from multiple modalities. In many healthcare environments, electronic images and reports are transmitted digitally via PACS, thus eliminating the need to manually file, retrieve, or transport film jackets. A standard format for PACS image storage and transfer is DICOM (Digital Imaging and Communications in Medicine). Non-image data, such as scanned documents, may be incorporated using various standard formats such as PDF (Portable Document Format) encapsulated in DICOM.

Although the DICOM standard describes methods for labeling body parts in the DICOM metafile associated with medical images, they are used inconsistently across disciplines and sites. Furthermore, not all medical images are DICOM compliant. Accurate identification of the body part or region of an examination is critical to downstream tasks like report generation and image comparison, and so inconsistent tagging poses a significant issue in many systems.

In various implementations, the examination type is stored in a database table integral to or separate from the a PACS. System automation and user preferences may all depend on the types of exams in such a table. For example, various data structures or master files may control features such as exam presentation protocols, which exams are selected for comparison, the attached default report template, patient forms that are presented, and forms for technologists. To configure system automation, an administrator may be required to set up links between each exam type and other exam types or these other data master files. This leads to significant complexity that can significantly delay deployment of enterprise imaging solutions and can result in mistakes that result in clinical inefficiencies or safety risks.

As more and more healthcare provider institutions consolidate, the table of available radiology exams often grows. Whereas an ideal radiology practice might include about 30 body parts, and as many as about 600-800 exam types, certain facilities may have 15,000 or more exam types. Setting 5-10 different sets of links between master files that may have several hundred items themselves to each of 15,000 exams types is inefficient and time consuming.

To resolve this problem, the present disclosure provides for examining the language in an exam description and other data sources such as stored concepts derived from image analytics, and categorizing each exam into a body part. Rather than depending solely on the body part designation in a DICOM header, which may not be present or may be inaccurate, in some embodiments, language in the exam description is evaluated. In some embodiments, image analytics and deep learning may be applied to even more accurately categorize exams types by body part. Watson image analytics is particularly suitable for such embodiments, as such a machine learning system may be otherwise performing analysis for other purposes. The present disclosure also includes a system for flexible configuration of the body parts themselves and rules by which keywords or concepts can be used to categorize exams by body part.

The present disclosure provides significant benefits by automatically reducing a facility's exam type organization to 30-40 body parts. Various other rules related to presentation of images, forms, or report templates, or for routing of exams to specific readers rely on creating links to exam types. Accordingly, by automatically classifying exams by body part, the number of required links is markedly reduced. As a result, there is a substantial reduction in the administrative burden and potential errors. The ability to pre-build an entire enterprise imaging system in the factory is enhanced. This approach is more efficient and results in higher quality than a manual approach of shipping hardware and software for configuration in the field.

The present disclosure thus provides a more efficient approach to project management and delivery of image management systems.

According to various embodiments of the present disclosure, prior studies are retrieved for comparison, and are screened based on anatomy. In order to arrive at the relevant anatomy, DICOM fields are tested against a list of strings that are indicative of the actual anatomy, irrespective of any mislabeling. These strings roughly correspond to clinical concepts, and so are resilient indicators of a study's subject. The list of strings may be predetermined manually, or it may be derived through data mining. In addition to manual checking, refiling of images in the course of use can be used as training data.

According to various embodiments of the present disclosure, a UI is also provided for rule creation, allowing the keywords and exam types to be user determined.

According to various embodiments of the present disclosure, a UI is provided for browsing studies, in which a positional indicator is displayed on a schematic image of the body for each available study. This UI may be used for training the system by prompting a user to drag a given image onto the body at the appropriate location.

According to various embodiments set forth herein, DICOM prefetch from external archives is provided. Various systems and methods set out herein have various advantages over alternative approaches. In particular, prefetch is made simpler via a limited number of rules; prefetch is made faster by only running a single query per new exam; prefetch is made accurate by associating a set of pre-defined keywords with each rule; prefetch is made easier to support and configure by eliminating any rule-per-exam-type requirement.

Accordingly, in various embodiments, a small set of rules is created that map any exam type to a specific body part and modality that define a single DICOM query. For example, a new “CR Chest” exam may use “CHEST” as the body part and “CR” as the modality. Further discussion of the rules and body parts is described in further detail below.

In various embodiments, a default set of keywords is provided that is associated with each body part. These defaults can be modified by system administrator and/or service personnel so each site can be optimally configured.

In various embodiments, a DICOM prefetch algorithm is provided that simplifies the DICOM query process by incorporating the above requirements into a single DICOM query per new exam.

In various embodiments, a test bed is provided to verify, validate and optimize pre-defined rules. The rules may still be modifiable, but the more accurate the pre-defined rules are initially, the more that a system can be delivered and considered to be prefetch ready out of the box.

In various embodiments, DICOM prefetch from an external archive is provided as an automatic process, which runs in the background.

Referring to FIG. 1, an exemplary PACS 100 consists of four major components. Various imaging modalities 101 . . . 109 such as computed tomography (CT) 101, magnetic resonance imaging (MRI) 102, or ultrasound (US) 103 provide imagery to the system. In some implementations, imagery is transmitted to a PACS Gateway 111, before being stored in archive 112. Archive 112 provides for the storage and retrieval of images and reports. Workstations 121 . . . 129 provide for interpreting and reviewing images in archive 112. In some embodiments, a secured network is used for the transmission of patient information between the components of the system. In some embodiments, workstations 121 . . . 129 may be web-based viewers. PACS delivers timely and efficient access to images, interpretations, and related data, eliminating the drawbacks of traditional film-based image retrieval, distribution, and display.

A PACS may handle images from various medical imaging instruments, such as X-ray plain film (PF), ultrasound (US), magnetic resonance (MR), Nuclear Medicine imaging, positron emission tomography (PET), computed tomography (CT), endoscopy (ES), mammograms (MG), digital radiography (DR), computed radiography (CR), Histopathology, or ophthalmology. However, a PACS is not limited to a predetermined list of images, and supports clinical areas beyond conventional sources of imaging such as radiology, cardiology, oncology, or gastroenterology.

Different users may have a different view into the overall PACS system. For example, while a radiologist may typically access a viewing station, a technologist may typically access a QA workstation.

In some implementations, the PACS Gateway 111 comprises a quality assurance (QA) workstation. The QA workstation provides a checkpoint to make sure patient demographics are correct as well as other important attributes of a study. If the study information is correct the images are passed to the archive 112 for storage. The central storage device, archive 112, stores images and in some implementations, reports, measurements and other information that resides with the images.

Once images are stored to archive 112, they may be accessed from reading workstations 121 . . . 129. The reading workstation is where a radiologist reviews the patient's study and formulates their diagnosis. In some implementations, a reporting package is tied to the reading workstation to assist the radiologist with dictating a final report. A variety of reporting systems may be integrated with the PACS, including those that rely upon traditional dictation. In some implementations, CD or DVD authoring software is included in workstations 121 . . . 129 to burn patient studies for distribution to patients or referring physicians.

In some implementations, a PACS includes web-based interfaces for workstations 121 . . . 129. Such web interfaces may be accessed via the internet or a Wide Area Network (WAN). In some implementations, connection security is provided by a VPN (Virtual Private Network) or SSL (Secure Sockets Layer). The client side software may comprise ActiveX, JavaScript, or a Java Applet. PACS clients may also be full applications which utilize the full resources of the computer they are executing on outside of the web environment.

Communication within PACS is generally provided via Digital Imaging and Communications in Medicine (DICOM). DICOM provides a standard for handling, storing, printing, and transmitting information in medical imaging. It includes a file format definition and a network communications protocol. The communication protocol is an application protocol that uses TCP/IP to communicate between systems. DICOM files can be exchanged between two entities that are capable of receiving image and patient data in DICOM format.

DICOM groups information into data sets. For example, a file containing a particular image, generally contains a patient ID within the file, so that the image can never be separated from this information by mistake. A DICOM data object consists of a number of attributes, including items such as name and patient ID, as well as a special attribute containing the image pixel data. Thus, the main object has no header as such, but instead comprises a list of attributes, including the pixel data. A DICOM object containing pixel data may correspond to a single image, or may contain multiple frames, allowing storage of cine loops or other multi-frame data. DICOM supports three- or four-dimensional data encapsulated in a single DICOM object. Pixel data may be compressed using a variety of standards, including JPEG, Lossless JPEG, JPEG 2000, and Run-length encoding (RLE). LZW (zip) compression may be used for the whole data set or just the pixel data.

Referring to FIG. 2, an exemplary PACS image search and retrieval method 200 is depicted. Communication with a PACS server, such as archive 112, is done through DICOM messages that that contain attributes tailored to each request. At 201, a client, such as workstation 121, establishes a network connection to a PACS server. At 202, the client prepares a DICOM message, which may be a C-FIND, C-MOVE, C-GET, or C-STORE request. At 203, the client fills in the DICOM message with the keys that should be matched. For example, to search by patient ID, a patient ID attribute is included. At 204, the client creates empty attributes for all the values that are being requested from the server. For example, if the client is requesting an image ID suitable for future retrieval of an image, it include an empty attribute for an image ID in the message. At 205, the client sends the message to the server. At 206, the server sends back to the client a list of one or more response messages, each of which includes a list of DICOM attributes, populated with values for each match.

An electronic health record (EHR), or electronic medical record (EMR), may refer to the systematized collection of patient and population electronically-stored health information in a digital format. These records can be shared across different health care settings and may extend beyond the information available in a PACS discussed above. Records may be shared through network-connected, enterprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.

EHR systems may be designed to store data and capture the state of a patient across time. In this way, the need to track down a patient's previous paper medical records is eliminated. In addition, an EHR system may assist in ensuring that data is accurate and legible. It may reduce risk of data replication as the data is centralized. Due to the digital information being searchable, EMRs may be more effective when extracting medical data for the examination of possible trends and long term changes in a patient. Population-based studies of medical records may also be facilitated by the widespread adoption of EHRs and EMRs.

Health Level-7 or HL7 refers to a set of international standards for transfer of clinical and administrative data between software applications used by various healthcare providers. These standards focus on the application layer, which is layer 7 in the OSI model. Hospitals and other healthcare provider organizations may have many different computer systems used for everything from billing records to patient tracking. Ideally, all of these systems may communicate with each other when they receive new information or when they wish to retrieve information, but adoption of such approaches is not widespread. These data standards are meant to allow healthcare organizations to easily share clinical information. This ability to exchange information may help to minimize variability in medical care and the tendency for medical care to be geographically isolated.

In various systems, connections between a PACS, Electronic Medical Record (EMR), Hospital Information System (HIS), Radiology Information System (RIS), or report repository are provided. In this way, records and reports form the EMR may be ingested for analysis. For example, in addition to ingesting and storing HL7 orders and results messages, ADT messages may be used, or an EMR, RIS, or report repository may be queried directly via product specific mechanisms. Such mechanisms include Fast Health Interoperability Resources (FHIR) for relevant clinical information. Clinical data may also be obtained via receipt of various HL7 CDA documents such as a Continuity of Care Document (CCD). Various additional proprietary or site-customized query methods may also be employed in addition to the standard methods.

Referring to FIG. 3, a schematic view of body part matching is provided according to embodiments of the present disclosure. As discussed above, there is a need to accurately and quickly retrieve (prefetch) prior exams when a new exam is scheduled. In alternative approaches, accurate prefetch methods are slow and both memory and bandwidth intensive, while fast prefetch methods are too inaccurate for practical usage.

In systems and methods according to the present disclosure, each new exam originating from a PACS 301 may have a single, unique body part or multiple body parts. Each body part is deconstructed 302 into a set of globally unique keywords. A DICOM server 303 responds with what studies match for a current patient. In some embodiments, additional filters are applied to the study retrieval. Such filters may be on the basis of modality, contrast, 2D or 3D, date, site or facility name, whether data is available online, or whether a study includes measurements. The results are analyzed to create a list of keywords 304 from the DICOM server. Those keywords are then matched against the globally unique keywords, for example by scanning through the keyword list. Exams where there is a keyword-pair match are pre-fetched, while other matches are dropped.

In some embodiments, the body parts and keywords are defined as follows. Each exam is assumed to have an exam type (e.g., “MAMMO DIGITIZED LEFT”). The exam type description is scanned for keywords. The keywords (e.g., “MAMMO”) are analyzed to determine a unique region or body part. In this example, the region is the breast. This region has an associated global keyword list (e.g., “BREAST”, “DUCTOG*”, “GALACTOG*”, “MAMM*”). When subsequent pre-fetch is run, any results from the DICOM server that contain any of the keywords from this global list are considered a match. Any results without a keyword may be dropped.

In some embodiments, the above process is performed for the modality of a current exam plus any other, user defined modalities. This allows a very detailed method of choosing what to accept and what to reject for pre-fetch. Additionally, this process may be set once with an initial configuration and allowed to run on an ongoing basis without further maintenance.

Referring to FIG. 4, an exemplary user interface 400 is depicted for the configuration of anatomical regions and associated keywords. In this example, regions 401 include the abdomen. When selected, keywords 402 corresponding to the abdomen are displayed for editing.

In various embodiments, a user may add body parts, and add or remove keywords for each body part. Keywords should generally be globally unique. However, in some circumstances, overlap may be permitted, such as with the term “Thora*” which may be used for both the “Thoracic Spine” body part and the “Chest” body part. In some embodiments, modalities and associated body parts and keywords are stored in a database.

Table 1 is a list of exemplary keywords and the associated body parts.

TABLE 1 Body Part Key Words Head Brain, head, auditor, posterior fossa, IAC, cereb, eye, pitu, CSF, tempor Skull Skull, sinus, mandib, maxil, nose, nasal, zygo, orbit, TMJ, mastoid, maxillofacial, dent, facial, crania Neck Neck, tong, laryn, parot, submand, oropharyn, nasopharyn, thyroid, carot Chest Chest, CxR, thora, lung, pulmonary, rib, sterno, Abdomen Abd, liver, hepat, spleen, pancrea, MRCP, bilia, gallbl, renal, reno, kidn, iliac, Meckels, aort, tipps, urog, cava, MIBG, IDA, Pelvis Pelv, vag, prostat, bladd, ureth, cysto, hystero, HSG, obstetr, OB_, uter, ovar, scrot, testi, Cardiac Card, heart, coronar, thall, mibi, Shoulder Shoulder, glenohumeral, acromioclav, AC_, scapul, clavic Elbow Elbow Wrist/Hand Wrist, hand, digit, fing, thumb, bone age, scaph Hip Hip, acetab, Knee Knee, patel, sunris, popli Foot/Ankle Foot, feet, ankle, toe, calcan, achil, tarsal GI Esophag, oeso, stomach, small bowel, duode, ilie, ileal, jeju, colo, anal, GI,_ UGI_, Barium enema, BE_, entero, defo, fistul, loopo, cholang, swall, speech, Upper Extremity Upper ext, extremity upper, ext upper, extr upper, arm, forear, humer, radiu, ulna Lower Extremity Lower ext, extremity lower, ext upper, extr upper, thigh, leg, calf, femu, femo, tibia, fibul, length Cervical Spine Cervic, odon, c spine, c-spine Thoracic Spine Thoracic spine, t spine, t-spine Lumbar Spine Lumbar, l spine, l-spine Sacrum/Coccyx Sacrum, sacral, coccyx, Breast Breast, mamm, stereot, ductog, tomosyn, galactog, hook Whole Body Whole body, skull to thigh, skull-to-thigh, bone surv, osseous surv, scoliosis DXA Dexa, DXA, QCT, densitom MISC PICC, facet, nerve, place, NG_, sclero, thromb, ather

It will be appreciated that while the above description focuses on keyword, mapping of body parts or regions according to various embodiments of the present disclosure may be based on additional features. For example, a user may be able to select concepts based on image analytics. In this way, image concepts or features can be used to classify an exam's body part or region. In various embodiments, DICOM header files are analyzed to extract keywords from the body part field or other fields that may give clues as to the body part. In various embodiments, a hierarchy of rules may be created to combine analysis of exam description keywords, DICOM header keywords, and image analytics concepts. In some such embodiments, each rule may carry a weight to determine precedence among rules.

Referring to FIG. 5, exemplary report output is provided. In this example, the indicated exam descriptions 501 have been matched to the indicated regions 502 according to the methods provided above.

Referring to FIG. 6-7, an exemplary user interface for configuring pre-fetch rules is provided. In FIG. 6, a list of pre-fetch rules 601 is presented. Each rule determines the regions and modalities to pre-fetch for a given exam modality of the present study. In FIG. 7, a user can input the pre-fetch rules. For a given modality, MG in this example, matching modalities, and matching body parts may be designated. In some embodiments, the prefetch rules are stored in a database table for retrieval.

Referring to FIG. 8, a method of examination prefetching is illustrated according to embodiments of the present disclosure. At 801, an anatomical region of a first medical imaging study is determined. At 802, a first plurality of keywords is determined corresponding to the anatomical region of the first medical imaging study. At 803, a plurality of studies is accessed having a patient in common with the first medical imaging study. At 804, a second plurality of keywords is extracted from the plurality of studies. At 805, those of the plurality of studies having extracted keywords in common with the first plurality of keywords are selected. At 806, the selected studies are pre-fetched for display to a user.

According to various embodiments of the present disclosure, configurable rules are provided that enable a user or organization to define body parts and associate keywords. It will be appreciated that keywords may comprise arbitrary strings of alphanumeric characters. In some embodiments, configurable rules are defined for any DICOM compliant system that can be configured for prefetching. In this way, the configurable rules capability may be applied to any number of third party archives in addition to proprietary systems or PACS. Accordingly, in some embodiments, a unique set of rules is maintained for each DICOM, PACS, or proprietary server that is configured for prefetch.

In some embodiments, the body part to keyword associations are learned from analysis of existing data sources in addition to being user configurable. In such embodiments, various machine learning techniques are suitable for learning the associations, such as association rule learning algorithms and deep learning. Such approaches are particularly useful for integration with unknown third party systems where data consistency and documentation cannot be relied upon.

In various embodiments, an organization's existing exam types are analyzed to determine how each exam type is processed into body parts. Based on this analysis, the keywords may be adjusted to improve the accuracy of the classification for a given organization.

In various embodiments, user selection of exams for comparison is tracked, for example through monitoring workstation access. By tracking which exams users select for comparison, the proper classification of exams into body parts can be continuously refined. This information allows determination of body parts and other exam characteristics (e.g., contrast use, isotope use, modality), which in turn can be used for continuously and automatically refine both the body part classification as well as the automated pre-fetch rules. In some embodiments, user selections are used for machine learning to associate keywords with body parts.

In some embodiments, a user interface is provided for a user to interactively correct misclassifications. As a user makes corrections, the system may adapt by automatically suggesting keywords modifications for user acceptance. In some embodiments, user corrections are used as input for machine learning.

In some embodiments, prefetch rules are applied to automatically display one or more exams for comparison when a first exam is displayed. In some embodiments, the automatic display is based on other defined rules based on user, user role, organization, site, patient characteristics or DICOM metafile information.

In some embodiments, the keyword based body part classification as provided herein is combined with image analytics in order to refine the rules via machine learning. In some embodiments, machine learning is applied to learn to classify exams by body part or determine more detailed exam characteristics such as whether contrast material was used.

Referring to FIG. 9, a method of examination prefetching is illustrated according to embodiments of the present disclosure. At 901, a plurality of keywords is read. Each of the plurality of keywords corresponds to an anatomical region. At 902, a plurality of studies is accessed. Each of the plurality of studies has at least one associated body part and an examination description. At 903, based upon the associated body part and examination description of each of the plurality of studies, the plurality of keywords is updated. At 904, in response to a request for a first medical imaging study, a plurality of studies having a patient in common with the first medical imaging study is accessed using the plurality of keywords. At 905, those of the plurality of studies related to the first medical imaging study by one or more rule are selected. At 906, the selected studies are pre-fetched for display to a user.

Referring to FIG. 10, a method of medical image access is illustrated according to embodiments of the present disclosure. At 1001, a first plurality of user-configurable rules is read from a data store. Each rule maps to a user-configurable anatomical region. At 1002, a plurality of studies is accessed from the image archive. Each of the plurality of studies has associated metadata. At 1003, the plurality of rules is applied to the metadata associated with the plurality of studies to determine an anatomical region of each of the plurality of studies. At 1004, based on the anatomical regions of the plurality of studies and one or more additional rule, a subset of the plurality of studies is selected for display to a user on a display.

In various embodiments, one or more medical imaging studies is accessed. Each study has one or more associated data elements that provide information related to an imaged body part or region. In various embodiments, data elements include text associated with the description of an exam, text extracted from fields in a DICOM metafile, text otherwise associated with the exam record, or information derived from analytics of the images themselves. Using a configurable mapping system that extracts keywords, character strings, or concepts in such data elements, one or more body parts or regions is assigned to the one or more medical imaging studies. Configurable rules are used to execute functions such as restoring exams from an archive, automatically displaying one or more exams in comparison to a primary exam that has a matching body part or region, sorting exams by body region or part, or prioritizing exams by body region or part. In various embodiments, matching criteria, sorting criteria, or other configuration parameters are determined by the rules.

Referring now to FIG. 11, a schematic of an example of a computing node is shown. Computing node 10 is only one example of a suitable computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, computing node 10 is capable of being implemented and/or performing any of the functionality set forth hereinabove.

In computing node 10 there is a computer system/server 12, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

As shown in FIG. 11, computer system/server 12 in computing node 10 is shown in the form of a general-purpose computing device. The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 

What is claimed is:
 1. A method comprising: reading a first plurality of user-configurable rules, each rule defining a mapping from one or more keywords to a user-configurable anatomical region; accessing a plurality of studies of a patient, each of the plurality of studies having associated metadata; applying the plurality of user-configurable rules to the metadata to determine an associated anatomical region of each of the plurality of studies; selecting a first medical imaging study of the plurality of studies having the associated anatomical region; after the associated anatomical region has been determined, determining a first plurality of keywords corresponding to the anatomical region of the first medical imaging study, wherein the first plurality of keywords comprises a name of the associated anatomical region supplemented with one or more additional keywords via machine learning comprising deep learning or association rule learning, the machine learning being trained to determine body part-to-keyword associations from labelled images and user corrections to the body part-to-keyword associations; receiving one or more additional rule from the user, the one or more additional rule for determining one or more regions and one or more modalities to pre-fetch, the one or more additional rule based on a modality of the first medical imaging study; applying the one or more additional rule to a plurality of prior studies of the patient having occurred before the first medical imaging study, thereby filtering the plurality of prior studies to a filtered set of prior studies; accessing the filtered set of prior studies; extracting a second plurality of keywords from the filtered set of prior studies, the second plurality of keywords comprising one or more keywords extracted from each study in the filtered set of prior studies; for each prior study in the filtered set of prior studies, when at least one of the one or more keywords from the filtered set of prior studies are in common with at least one of the one or more additional keywords, selecting that prior study for pre-fetching thereby further filtering the filtered set of prior studies; and pre-fetching any selected studies of the filtered set of prior studies for display to a user.
 2. The method of claim 1, wherein the metadata include exam descriptions, DICOM header information, or image attributes.
 3. The method of claim 1, wherein the one or more of the filtered set of prior studies are displayed to the user for comparison.
 4. The method of claim 1, further comprising: receiving from the user an indication of which of the selected one or more of the filtered set of prior studies are comparable to a predetermined medical image study.
 5. The method of claim 4, further comprising: updating the plurality of user-configurable rules based on the indication from the user.
 6. The method of claim 5, wherein updating the plurality of user-configurable rules comprises applying machine learning.
 7. The method of claim 1, further comprising: displaying to the user potential updates to the plurality of rules; receiving from the user an indication of approval or rejection of the potential updates.
 8. The method of claim 2, wherein the examination descriptions are contained in image headers.
 9. The method of claim 8, wherein the image headers are DICOM headers.
 10. The method of claim 1, further comprising: applying a filter to the selected studies prior to pre-fetching.
 11. The method of claim 10, wherein the filter is based on modality, study date, one or more DICOM attribute, 2D or 3D, or site name.
 12. A computer program product for medical image access, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: reading a first plurality of user-configurable rules, each rule defining a mapping from one or more keywords to a user-configurable anatomical region; accessing a plurality of studies of a patient, each of the plurality of studies having associated metadata; applying the plurality of user-configurable rules to the metadata to determine an associated anatomical region of each of the plurality of studies; selecting a first medical imaging study of the plurality of studies having the associated anatomical region; after the associated anatomical region has been determined, determining a first plurality of keywords corresponding to the anatomical region of the first medical imaging study, wherein the first plurality of keywords comprises a name of the associated anatomical region supplemented with one or more additional keywords via machine learning comprising deep learning or association rule learning, the machine learning being trained to determine body part-to-keyword associations from labelled images and user corrections to the body part-to-keyword associations; receiving one or more additional rule from the user, the one or more additional rule for determining one or more regions and one or more modalities to pre-fetch, the one or more additional rule based on a modality of the first medical imaging study; applying the one or more additional rule to a plurality of prior studies of the patient having occurred before the first medical imaging study, thereby filtering the plurality of prior studies to a filtered set of prior studies; accessing the filtered set of prior studies; extracting a second plurality of keywords from the filtered set of prior studies, the second plurality of keywords comprising one or more keywords extracted from each study in the filtered set of prior studies; for each prior study in the filtered set of prior studies, when at least one of the one or more keywords from the filtered set of prior studies are in common with at least one of the one or more additional keywords, selecting that prior study for pre-fetching thereby further filtering the filtered set of prior studies; and pre-fetching any selected studies of the filtered set of prior studies for display to a user.
 13. The computer program product of claim 12, wherein the metadata include exam descriptions, DICOM header information, or image attributes.
 14. The computer program product of claim 12, wherein the one or more of the filtered set of prior studies are displayed to the user for comparison.
 15. The computer program product of claim 12, the method further comprising: receiving from the user an indication of which of the selected one or more of the filtered set of prior studies are comparable to a predetermined medical image study.
 16. The computer program product of claim 15, the method further comprising: updating the plurality of user-configurable rules based on the indication from the user.
 17. The computer program product of claim 15, wherein updating the plurality of user-configurable rules comprises applying machine learning.
 18. The computer program product of claim 1, the method further comprising: displaying to the user potential updates to the plurality of rules; receiving from the user an indication of approval or rejection of the potential updates.
 19. A system comprising: an image archive; a display; a data store; a computing node having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: reading a first plurality of user-configurable rules from the data store, each rule defining a mapping from one or more keywords to a user-configurable anatomical region; accessing a plurality of studies from the image archive, each of the plurality of studies having associated metadata; applying the plurality of user-configurable rules to the metadata to determine an associated anatomical region of each of the plurality of studies; selecting a first medical imaging study of the plurality of studies having the associated anatomical region; after the associated anatomical region has been determined, determining a first plurality of keywords corresponding to the anatomical region of the first medical imaging study, wherein the first plurality of keywords comprises a name of the associated anatomical region supplemented with one or more additional keywords via machine learning comprising deep learning or association rule learning, the machine learning being trained to determine body part-to-keyword associations from labelled images and user corrections to the body part-to-keyword associations; receiving one or more additional rule from the user, the one or more additional rule for determining one or more regions and one or more modalities to pre-fetch, the one or more additional rule based on a modality of the first medical imaging study; applying the one or more additional rule to a plurality of prior studies of the patient having occurred before the first medical imaging study, thereby filtering the plurality of prior studies to a filtered set of prior studies; accessing the filtered set of prior studies; extracting a second plurality of keywords from the filtered set of prior studies, the second plurality of keywords comprising one or more keywords extracted from each study in the filtered set of prior studies; for each prior study in the filtered set of prior studies, when at least one of the one or more keywords from the filtered set of prior studies are in common with at least one of the one or more additional keywords, selecting that prior study for pre-fetching thereby further filtering the filtered set of prior studies; and pre-fetching any selected studies of the filtered set of prior studies for display to a user on the display. 